An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data

TL;DR

This paper introduces a reweighted nearest neighbor algorithm tailored for imbalanced data that optimizes the F-Measure, demonstrating superior performance especially when combined with sampling methods.

Contribution

The paper presents a novel reweighting scheme for NN that adjusts decision boundaries to better handle class imbalance and optimize the F-Measure.

Findings

Effective on multiple public imbalanced datasets

Outperforms standard NN methods in F-Measure

Combines well with sampling techniques for improved results

Abstract

In this paper, we address the challenging problem of learning from imbalanced data using a Nearest-Neighbor (NN) algorithm. In this setting, the minority examples typically belong to the class of interest requiring the optimization of specific criteria, like the F-Measure. Based on simple geometrical ideas, we introduce an algorithm that reweights the distance between a query sample and any positive training example. This leads to a modification of the Voronoi regions and thus of the decision boundaries of the NN algorithm. We provide a theoretical justification about the weighting scheme needed to reduce the False Negative rate while controlling the number of False Positives. We perform an extensive experimental study on many public imbalanced datasets, but also on large scale non public data from the French Ministry of Economy and Finance on a tax fraud detection task, showing that our method is very effective and, interestingly, yields the best performance when combined with state of the art sampling methods.